BioSkills bio-workflows-riboseq-pipeline
End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation.
install
source · Clone the upstream repo
git clone https://github.com/GPTomics/bioSkills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/GPTomics/bioSkills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/workflows/riboseq-pipeline" ~/.claude/skills/gptomics-bioskills-bio-workflows-riboseq-pipeline && rm -rf "$T"
manifest:
workflows/riboseq-pipeline/SKILL.mdsource content
Version Compatibility
Reference examples tested with: Bowtie2 2.5.3+, STAR 2.7.11+, cutadapt 4.4+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
thenpip show <package>
to check signatureshelp(module.function) - CLI:
then<tool> --version
to confirm flags<tool> --help
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Ribo-seq Pipeline
"Analyze my ribosome profiling data from FASTQ to translation efficiency" → Orchestrate adapter trimming, rRNA depletion, genome alignment, periodicity QC, ORF detection (RiboCode), stalling analysis, and translation efficiency estimation (riborex).
Pipeline Overview
FASTQ → Preprocessing → rRNA removal → Alignment → P-site → TE → ORF calling
Step 1: Preprocessing
# Remove adapters cutadapt -a CTGTAGGCACCATCAAT \ --minimum-length 25 --maximum-length 35 \ -o trimmed.fastq.gz reads.fastq.gz # Remove rRNA bowtie2 -x rRNA_index --un non_rrna.fastq.gz -U trimmed.fastq.gz
Step 2: Alignment
# Align to transcriptome STAR --genomeDir star_index \ --readFilesIn non_rrna.fastq.gz \ --readFilesCommand zcat \ --outFilterMismatchNmax 2 \ --alignEndsType EndToEnd \ --outSAMtype BAM SortedByCoordinate
Step 3: P-site Calibration
from plastid import BAMGenomeArray # Build metagene profile metagene_generate annotation.gtf ribo.bam metagene_output/ # Calculate P-site offsets psite annotation.gtf metagene_output/profile.txt psite_offsets.txt
Step 4: Translation Efficiency
# TE = Ribo-seq RPKM / RNA-seq RPKM from plastid import BAMGenomeArray import numpy as np ribo_counts = count_reads(ribo_bam, genes) rna_counts = count_reads(rna_bam, genes) te = ribo_counts / rna_counts
Step 5: ORF Detection
# RiboCode for ORF calling RiboCode -a annotation.gtf -c config.txt -o ribocoded_orfs
Related Skills
- ribo-seq/ - Individual Ribo-seq analysis skills
- differential-expression - For differential TE